This package provides a self-contained, static build of the HDF5 (Hierarchical Data Format 5) C library (release 2.1.1) for R package developers. Designed for use in the LinkingTo field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their HDF5 plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard HDF5 API while keeping the underlying third-party headers fully encapsulated. HDF5 is developed by The HDF Group <https://www.hdfgroup.org/>.

Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.

Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.

This package provides a set of tools to create georeferenced hillshade relief raster maps using ray-tracing and other advanced hill-shading techniques. It includes a wrapper function to create a georeferenced, ray-traced hillshade map from a digital elevation model, and other functions that can be used in a rayshader pipeline.

This package provides a correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on hdImpute', Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on missForest', and Mayer (2022) <https://github.com/mayer79/missRanger> for more on missRanger'.

This package provides univariate and indexed (multivariate) nonparametric smoothed kernel estimators for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025) <doi:10.1093/biomet/asaf008>.

Set of R functions to be coupled with the xeus-r jupyter kernel in order to drive execution of code in notebook input cells, how R objects are to be displayed in output cells, and handle two way communication with the front end through comms.

Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion. The main idea is to use changing intensity ratios of ion pairs from peak list generated with xcms as candidates and evaluate those against base peak chromatograms and spectra information within the raw measurement data automatically. The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.

This package provides a lightweight framework for creating high quality, complex heatmaps using base graphics. Supports hierarchical clustering with dendrograms, column and row scaling, cluster sub-divisions, customizable cell colours, shapes and sizes, legends, and flexible layouts for arranging multiple heatmaps. Designed to return plot objects that can be easily arranged with other plots without sacrificing resolution. Methods for hierarchical clustering and distance computations are described in Murtagh and Contreras (2012) <doi:10.1002/wics.53>. Dendrogram visualisation methods are based on the ggdendro package by de Vries and Ripley (2020) <https://CRAN.R-project.org/package=ggdendro>.

Generates HIDECAN plots that summarise and combine the results of genome-wide association studies (GWAS) and transcriptomics differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot is presented in Angelin-Bonnet et al. (2023) (currently in review).

Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.

The theoretical covariance between pairs of markers is calculated from either paternal haplotypes and maternal linkage disequilibrium (LD) or vise versa. A genetic map is required. Grouping of markers is based on the correlation matrix and a representative marker is suggested for each group. Employing the correlation matrix, optimal sample size can be derived for association studies based on a SNP-BLUP approach. The implementation relies on paternal half-sib families and biallelic markers. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations" <doi:10.1186/s12863-020-00871-1>. Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium" <doi:10.1086/381000>.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed HTGM3D', a three-dimensional version of GoMiner'.

Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.

Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.

The half-weight index gregariousness (HWIG) is an association index used in social network analyses. It extends the half-weight association index (HWI), correcting for level of gregariousness in individuals. It is calculated using group by individual data according to methods described in Godde et al. (2013) <doi:10.1016/j.anbehav.2012.12.010>.

Add, share and manage annotations for Shiny applications and R Markdown documents via hypothes.is'.

Reporting heritability estimates is an important to quantitative genetics studies and breeding experiments. Here we provide functions to calculate various broad-sense heritabilities from asreml and lme4 model objects. All methods we have implemented in this package have extensively discussed in the article by Schmidt et al. (2019) <doi:10.1534/genetics.119.302134>.

Convert Chinese characters into Hanyu Pinyin (the official romanization system for Standard Chinese) with support for tones, toneless output, initials, URL slugs, and valid R variable names. The package was inspired by the now-orphaned CRAN package pinyin (archived in April 2026 after the maintainer became unreachable). hanyupinyin is a ground-up rewrite using the authoritative Unicode Unihan database, a vectorized engine, and modern R practices. Dictionary data are derived from the Unicode Unihan Database (Unicode Consortium, 2025) <https://www.unicode.org/reports/tr38/>.

In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts. Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.

This package provides a collection of reweighted marginal hypothesis tests for clustered data, based on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>. The tests in this collection are clustered analogs to well-known hypothesis tests in the classical setting, and are appropriate for data with cluster- and/or group-size informativeness. The syntax and output of functions are modeled after common, recognizable functions native to R. Methods used in the package refer to Gregg, M., Datta, S., and Lorenz, D. (2020) <doi:10.1177/0962280220928572>, Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288> Dutta, S. and Datta, S. (2015) <doi:10.1111/biom.12447>, Lorenz, D., Datta, S., and Harkema, S. (2011) <doi:10.1002/sim.4368>, Datta, S. and Satten, G. (2008) <doi:10.1111/j.1541-0420.2007.00923.x>, Datta, S. and Satten, G. (2005) <doi:10.1198/016214504000001583>.

This package implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) <doi:10.1002/rra.3978>. The identification of associated events is based on the event detection implemented in hydropeak'.

Events from individual hydrologic time series are extracted, and events are matched across multiple time series. The package has been applied in studies such as Wasko and Guo (2022) <doi:10.1002/hyp.14563> and Mohammadpour Khoie, Guo and Wasko (2025) <doi:10.1016/j.envsoft.2025.106521>.